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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
RNA Stability01:53

RNA Stability

Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
Types of RNA01:23

Types of RNA

Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...

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Related Experiment Video

Updated: Jun 6, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Predicting RNA-binding residues from evolutionary information and sequence conservation.

Yu-Feng Huang1, Li-Yuan Chiu, Chun-Chin Huang

  • 1Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China. yfhuang@csie.ntu.edu.tw

BMC Genomics
|December 15, 2010
PubMed
Summary

This study introduces ProteRNA, a computational tool that predicts RNA-binding residues in RNA-binding proteins using machine learning and conserved residue analysis. This aids in understanding protein function and guiding experimental mutagenesis.

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Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)
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Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)

Published on: July 10, 2019

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Related Experiment Videos

Last Updated: Jun 6, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)
09:26

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)

Published on: July 10, 2019

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology
  • Bioinformatics

Background:

  • RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation.
  • RBPs recognize specific RNA sequences through diverse structural arrangements of RNA-binding domains (RBDs).
  • Identifying RNA-binding residues is crucial for understanding RBP function and guiding experimental studies.

Purpose of the Study:

  • To develop a computational framework for predicting RNA-binding residues in RBPs.
  • To leverage machine learning and pattern mining for accurate residue identification.
  • To assist biologists in site-directed mutagenesis experiments.

Main Methods:

  • The ProteRNA framework combines a Support Vector Machine (SVM) classifier with WildSpan for conserved residue discovery.
  • The approach focuses on identifying residues likely to interact with RNA.
  • Sequence-based analysis is employed for prediction.

Main Results:

  • ProteRNA achieved an overall accuracy of 89.78% on an independent testing dataset.
  • Performance metrics include MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546.
  • Conserved residues identified by the method provide insights into functionally important sites.

Conclusions:

  • ProteRNA is a novel sequence-based predictor for identifying RNA-binding residues in RBPs.
  • The combination of machine learning and pattern mining enhances prediction accuracy.
  • This tool facilitates the study of RBP function and experimental validation.